The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Mar. 01, 2022
Filed:
Aug. 07, 2018
Indigo Ag, Inc., Boston, MA (US);
David Patrick Perry, Boston, MA (US);
Geoffrey Albert von Maltzahn, Boston, MA (US);
Robert Berendes, Riehen, CH;
Eric Michael Jeck, San Mateo, CA (US);
Barry Loyd Knight, Cordova, TN (US);
Rachel Ariel Raymond, Arlington, VA (US);
Ponsi Trivisvavet, Lexington, MA (US);
Justin Y H Wong, Boston, MA (US);
Neal Hitesh Rajdev, Lincoln, MA (US);
Marc-Cedric Joseph Meunier, Dover, MA (US);
Casey James Leist, Cambridge, MA (US);
Pranav Ram Tadi, Corona, CA (US);
Andrea Lee Flaherty, North Grafton, MA (US);
Charles David Brummitt, Cambridge, MA (US);
Naveen Neil Sinha, Somerville, MA (US);
Jordan Lambert, Cambridge, MA (US);
Jonathan Hennek, Medford, MA (US);
Carlos Becco, Buenos Aires, AR;
Mark Allen, Sydney, AU;
Daniel Bachner, Sao Paulo, BR;
Fernando Derossi, Boston, MA (US);
Ewan Lamont, Charlestown, MA (US);
Rob Lowenthal, Brooklyn, NY (US);
Dan Creagh, Chicago, IL (US);
Steve Abramson, Waltham, MA (US);
Ben Allen, Cedar Park, TX (US);
Jyoti Shankar, Charlestown, MA (US);
Chris Moscardini, Amesbury, MA (US);
Jeremy Crane, Boston, MA (US);
David Weisman, Somerville, MA (US);
Gerard Keating, Winchester, MA (US);
Lauren Moores, Charlestown, MA (US);
William Pate, Kingston, NH (US);
INDIGO AG, INC., Boston, MA (US);
Abstract
A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.